使用MapReduce实现knn算法

本文主要是介绍使用MapReduce实现knn算法，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

算法的流程

（1）首先将训练集以共享文件的方式分发到各个map节点

（2）每一个map节点主要<LongWritable ,Text,,LongWritable,ListWritable<DoubleWritable>> LongWritable 主要就是文件的偏移地址，保证唯一。ListWritable主要就是最近的类别。

Reduce节点主要计算出，每一个要预测节点的类别。

package knn;

public class Distance {

public static double EuclideanDistance(double[] a, double[] b)
throws Exception {
if (a.length != b.length)
throw new Exception("size not compatible!");
double sum = 0.0;
for (int i = 0; i < a.length; i++) {
sum += Math.pow(a[i] - b[i], 2);
}
return Math.sqrt(sum);
}
}

package knn;

import java.io.BufferedReader;

/**
* KNearestNeigbour Classifier each instance in training set is of form
* a1,a2,a3...an,l1 in which l1 represents the label. and each instance in
* predict set is of form a1,a2,a3...an,-1,in which -1 is the label we want to
* specify. In my algorithm,I assume that the trainning set is relatively small
* so we can load them in memory and the predict set is large another thing we
* need to pay attention to is that all our test instances are all in one file
* so that the index of line is unique to each instance.
*
*/
public class KNearestNeighbour {
public static class KNNMap
extends
Mapper<LongWritable, Text, LongWritable, ListWritable<DoubleWritable>> {
private int k;
private ArrayList<Instance> trainSet;

@Override
protected void setup(Context context) throws IOException,
InterruptedException {
k = context.getConfiguration().getInt("k", 1);
trainSet = new ArrayList<Instance>();

Path[] trainFile = DistributedCache.getLocalCacheFiles(context
.getConfiguration());
// add all the tranning instances into attributes
BufferedReader br = null;
String line;
for (int i = 0; i < trainFile.length; i++) {
br = new BufferedReader(new FileReader(trainFile[0].toString()));
while ((line = br.readLine()) != null) {
Instance trainInstance = new Instance(line);
System.out.println(trainInstance.toString());
trainSet.add(trainInstance);
}
}
}

/**
* find the nearest k labels and put them in an object of type
* ListWritable. and emit <textIndex,lableList>
*/
@Override
public void map(LongWritable textIndex, Text textLine, Context context)
throws IOException, InterruptedException {
System.out.println(textLine.toString());
// distance stores all the current nearst distance value
// . trainLable store the corresponding lable
ArrayList<Double> distance = new ArrayList<Double>(k);
ArrayList<DoubleWritable> trainLable = new ArrayList<DoubleWritable>(
k);
for (int i = 0; i < k; i++) {
distance.add(Double.MAX_VALUE);
trainLable.add(new DoubleWritable(-1.0));
}
ListWritable<DoubleWritable> lables = new ListWritable<DoubleWritable>(
DoubleWritable.class);
Instance testInstance = new Instance(textLine.toString());
for (int i = 0; i < trainSet.size(); i++) {
try {
double dis = Distance.EuclideanDistance(trainSet.get(i)
.getAtrributeValue(), testInstance
.getAtrributeValue());
int index = indexOfMax(distance);
if (dis < distance.get(index)) {
distance.remove(index);
trainLable.remove(index);
distance.add(dis);
trainLable.add(new DoubleWritable(trainSet.get(i)
.getLable()));
}
} catch (Exception e) {
e.printStackTrace();
}
}
lables.setList(trainLable);
context.write(textIndex, lables);
}

/**
* return the index of the maximum number of an array
*
* @param array
* @return
*/
public int indexOfMax(ArrayList<Double> array) {
int index = -1;
Double min = Double.MIN_VALUE;
for (int i = 0; i < array.size(); i++) {
if (array.get(i) > min) {
min = array.get(i);
index = i;
}
}
return index;
}
}

public static class KNNReduce
extends
Reducer<LongWritable, ListWritable<DoubleWritable>, NullWritable, DoubleWritable> {

@Override
public void reduce(LongWritable index,
Iterable<ListWritable<DoubleWritable>> kLables, Context context)
throws IOException, InterruptedException {
/**
* each index can actually have one list because of the assumption
* that the particular line index is unique to one instance.
*/
DoubleWritable predictedLable = new DoubleWritable();
for (ListWritable<DoubleWritable> val : kLables) {
try {
predictedLable = valueOfMostFrequent(val);
break;
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
context.write(NullWritable.get(), predictedLable);
}

public DoubleWritable valueOfMostFrequent(
ListWritable<DoubleWritable> list) throws Exception {
if (list.isEmpty())
throw new Exception("list is empty!");
else {
HashMap<DoubleWritable, Integer> tmp = new HashMap<DoubleWritable, Integer>();
for (int i = 0; i < list.size(); i++) {
if (tmp.containsKey(list.get(i))) {
Integer frequence = tmp.get(list.get(i)) + 1;
tmp.remove(list.get(i));
tmp.put(list.get(i), frequence);
} else {
tmp.put(list.get(i), new Integer(1));
}
}
// find the value with the maximum frequence.
DoubleWritable value = new DoubleWritable();
Integer frequence = new Integer(Integer.MIN_VALUE);
Iterator<Entry<DoubleWritable, Integer>> iter = tmp.entrySet()
.iterator();
while (iter.hasNext()) {
Map.Entry<DoubleWritable, Integer> entry = (Map.Entry<DoubleWritable, Integer>) iter
.next();
if (entry.getValue() > frequence) {
frequence = entry.getValue();
value = entry.getKey();
}
}
return value;
}
}
}

public static void main(String[] args) throws IOException,
InterruptedException, ClassNotFoundException {
Job kNNJob = new Job();
kNNJob.setJobName("kNNJob");
kNNJob.setJarByClass(KNearestNeighbour.class);
DistributedCache.addCacheFile(URI.create(args[2]), kNNJob
.getConfiguration());
kNNJob.getConfiguration().setInt("k", Integer.parseInt(args[3]));

kNNJob.setMapperClass(KNNMap.class);
kNNJob.setMapOutputKeyClass(LongWritable.class);
kNNJob.setMapOutputValueClass(ListWritable.class);

kNNJob.setReducerClass(KNNReduce.class);
kNNJob.setOutputKeyClass(NullWritable.class);
kNNJob.setOutputValueClass(DoubleWritable.class);

kNNJob.setInputFormatClass(TextInputFormat.class);
kNNJob.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(kNNJob, new Path(args[0]));
FileOutputFormat.setOutputPath(kNNJob, new Path(args[1]));

kNNJob.waitForCompletion(true);
System.out.println("finished!");
}
}

package knn;

public class Instance {
private double[] attributeValue;
private double lable;

/**
* a line of form a1 a2 ...an lable
*
* @param line
*/
public Instance(String line) {
System.out.println(line);
String[] value = line.split(" ");
attributeValue = new double[value.length - 1];
for (int i = 0; i < attributeValue.length; i++) {
attributeValue[i] = Double.parseDouble(value[i]);
System.out.print(attributeValue[i] + "\t");
}
lable = Double.parseDouble(value[value.length - 1]);
System.out.println(lable);
}

public double[] getAtrributeValue() {
return attributeValue;
}

public double getLable() {
return lable;
}
}

package knn;

import java.io.DataInput;

public class ListWritable<T extends Writable> implements Writable {
private List<T> list;
private Class<T> clazz;

public ListWritable() {
list = null;
clazz = null;
}

public ListWritable(Class<T> clazz) {
this.clazz = clazz;
list = new ArrayList<T>();
}

public void setList(List<T> list) {
this.list = list;
}

public boolean isEmpty() {
return list.isEmpty();
}

public int size() {
return list.size();
}

public void add(T element) {
list.add(element);
}

public void add(int index, T element) {
list.add(index, element);
}

public T get(int index) {
return list.get(index);
}

public T remove(int index) {
return list.remove(index);
}

public void set(int index, T element) {
list.set(index, element);
}

@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(clazz.getName());
out.writeInt(list.size());
for (T element : list) {
element.write(out);
}
}

@SuppressWarnings("unchecked")
@Override
public void readFields(DataInput in) throws IOException {
try {
clazz = (Class<T>) Class.forName(in.readUTF());
} catch (ClassNotFoundException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
int count = in.readInt();
this.list = new ArrayList<T>();
for (int i = 0; i < count; i++) {
try {
T obj = clazz.newInstance();
obj.readFields(in);
list.add(obj);
} catch (InstantiationException e) {
e.printStackTrace();
} catch (IllegalAccessException e) {
e.printStackTrace();
}
}
}

}